Medical imaging modalities such as computed tomography (CT), magnetic resonance (MR), ultrasound (US), single photon emission computed tomography (SPECT), positron emission tomography (PET), and x-ray can play an important role in the diagnosis of diseases such as cancer. For instance, they can be used to non-invasively obtain information indicative of physiological tissue in the body, and such information can be used to facilitate determining whether a tumor is benign or malignant. Such non-invasive techniques typically are less risky and costly than an invasive technique such as a biopsy. In addition, for relatively small tumors, such as tumors 10 millimeters or less, it may be relatively difficult to ascertain whether a biopsy needle hit the tumor.
More particularly, images such as CT images can be used to perform a differential diagnosis. By way of example, two CT images, both including information indicative of the same tumor, but generated from data acquired at a different moment in time, for example one to six months apart, can be used to access tumor growth over time by comparing the size of the tumor in the first image with the size of the same tumor in the second image. Generally, an increase in tumor size greater than a pre-set threshold (e.g., 20%) indicates that the tumor is malignant, whereas non-growth or growth less than the threshold indicates that the tumor is benign.
Unfortunately, when comparing such images some organs such as the lung may not be in the same position in both images due to differences in patient setup. As a consequence, spatial registration between the images may be problematic. For example, the clinician may have to manually review a number of images (e.g., 200 or more) in a second set of images, generated with data acquired in a second scan, in order to find an image that shows the tumor for comparison with a first image from a first scan. Even after spatial registration, structures inside the lungs such as the tumor may not be in the same location due to differences in the respiratory state.
Registration algorithms are widely used as base technology to facilitate side-by-side image comparison, e.g., in tumor follow-up or for comparison of multi-modal images. The aim of many registration algorithms is to minimize a given similarity term, e.g., a difference of image gray-values, constrained by a regularization model, which allows only for reasonable transformations (also called deformations).
Usually, a registration algorithm runs without any user input. However, for challenging tasks, e.g., CT and 3D ultrasound, landmarks, which are generally set by the user, can be employed to facilitate the registration. Landmark-based registration can further be applied to locally recover failed registration results. In this case supplementary information in form of landmarks is provided and employed during a second-pass registration as additional constraints. Such registration algorithms are e.g. known from US 2013/0329980 A1 or US 2010/0260392 A1.